The NVIDIA RTX 3090 Ti, while a powerful GPU, falls short of the VRAM requirements for running the Qwen 2.5 72B model, even with INT8 quantization. Qwen 2.5 72B, a large language model with 72 billion parameters, necessitates substantial memory to store the model weights and intermediate activations during inference. Quantization to INT8 reduces the VRAM footprint compared to FP16, but still requires approximately 72GB of VRAM. The RTX 3090 Ti offers only 24GB of VRAM, creating a significant shortfall of 48GB. This means the entire model cannot be loaded onto the GPU at once, leading to out-of-memory errors and preventing successful inference.
Due to the VRAM limitations, running Qwen 2.5 72B on a single RTX 3090 Ti is not feasible. Consider these options: 1) **GPU Clustering/Multi-GPU setup:** Utilize multiple GPUs with sufficient combined VRAM to accommodate the model. This requires specialized software and expertise. 2) **CPU Offloading:** Offload some layers of the model to the CPU, which uses system RAM. This will significantly slow down inference speed. 3) **Smaller Model:** Opt for a smaller language model that fits within the RTX 3090 Ti's VRAM. Models with fewer parameters, such as Qwen 1.5 14B, or quantized versions of similar size models, might be viable alternatives. 4) **Cloud-based Inference:** Utilize cloud-based services that offer access to powerful GPUs with ample VRAM.